Institut für Physikalische Chemie, Christian-Albrechts-University, Olshausenstr. 40, 24098, Kiel, Germany.
J Comput Chem. 2013 Sep 30;34(25):2178-89. doi: 10.1002/jcc.23382. Epub 2013 Jul 15.
We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code that is parallelized across reference data items via the message-passing interface (MPI). Details of GA tuning turn-ed out to be far less important for global optimization efficiency than using suitable ranges within which the parameters are varied. To establish these ranges, either prior knowledge can be used or successive stages of GA optimizations, each building upon the best parameter vectors and ranges found in the previous stage. We have finally arrive-ed at optimized force fields with smaller error measures than those published previously. Hence, this optimization approach will contribute to converting force-field fitting from a specialist task to an everyday commodity, even for the more difficult case of reactive force fields.
我们已经使用无偏全局优化方法将反应力场拟合到给定的参考数据集。具体来说，我们使用遗传算法 (GA) 通过消息传递接口 (MPI) 跨参考数据项并行化的内部 GA 代码，将 ReaxFF 拟合到 SiOH 数据。GA 调优的细节对于全局优化效率的重要性远不如在参数变化的合适范围内使用合适的范围。要建立这些范围，可以使用先验知识，也可以使用 GA 优化的连续阶段，每个阶段都基于前一个阶段中找到的最佳参数向量和范围。我们最终得到了优化的力场，其误差度量值小于以前发表的值。因此，这种优化方法将有助于将力场拟合从一项专业任务转化为日常商品，即使对于更困难的反应力场情况也是如此。
